Fullpower is seeking a passionate, team-oriented, and self-motivated Data Scientist interested in working on our next generation of products and algorithms and modeling rich, biomedical sensor data with a view toward medical, health, and fitness outcomes.
The ideal candidate will have a strong background in machine learning and data science and a proven track record of deploying models and algorithms for real-world applications.
Job Responsibilities:
- Work with health data and time-series sensor data
- Connect biomedical sensor data with medical, health, and fitness outcomes
- Research and development of machine learning algorithms and statistical models for non-invasive sleep tracking
- Develop visualizations and tools for understanding and annotating data
- Optimize algorithms for running on embedded devices and in the cloud
- Design and run experiments and clinical trials
- Processing, cleaning, and combining different data sources to create new datasets
- Collaborate with an interdisciplinary team of scientists, engineers, mathematicians for quick deployment of solutions
Software skills:
- Expertise in Python and packages such as numpy, pandas, scikit-learn
- Experience with a deep learning framework such as Tensorflow, pyTorch, or equivalent
- Experience working with databases including familiarity with SQL
- Experience with Amazon Web Services (AWS) or Google Cloud
- Experience with C++ is a plus
Leadership qualities:
- Self starter
- Strong interpersonal and communication skills
Education and Experience:
- Experience applying data science and machine learning to real-world problems
- Bachelor's degree or higher in Mathematics, Statistics, Physics, Computer Science, or equivalent
- Graduate degree and/or 5+ years experience a plus
Additional Information:
- Fullpower does not sponsor work visas - must already be authorized to work in the United States
- The base salary range for this full-time position is $130,000 to $170,000, plus equity, plus benefits. Fullpower's salary ranges are determined by role and level. Within the range, individual pay is determined by additional factors, including job-related skills, experience, and relevant education and training.
